What is NLP in simple terms?

NLP is the field of building computer systems that classify, search, transform or generate written and spoken human language.

What is a token in NLP?

A token is a unit processed by a model, such as a word, subword, character or punctuation mark. Tokenization rules depend on the model.

What is an embedding?

An embedding is a dense numerical vector used to represent a token, sentence or document so a model can learn or compare useful relationships.

What are the main NLP tasks?

Common tasks include classification, entity recognition, semantic search, question answering, summarization, translation and text generation.

Is NLP the same as a large language model?

No. Large language models are one family of models used in NLP. NLP also includes rules, statistical methods, search, smaller neural models and structured pipelines.

What is the difference between training and inference?

Training updates model parameters from data. Inference applies a trained model to new input without changing its parameters.

How should a beginner evaluate an NLP model?

Start with task-specific metrics and inspect errors by class, language and operating condition. Add human review and downstream outcome measures where decisions matter.

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自然言語処理の基礎を理解する

自然言語処理(NLP)は言語学、コンピュータサイエンス、人工知能を交差させる魅力的な分野です。テキストを分析し、応答を生成し、よりはるかに多くを生成することができるアプリケーションを開発するための重要な機械が人間言語を理解し、解釈することを可能にします.